On this page: Degree Requirements • Required Course Information • 39 Credit Hours of Elective Courses • Typical Schedule • Teaching Requirement • Comprehensive Exam • Depth Qualifying Exam (DQE) • PhD Dissertation
Degree Requirements
Degree requirements for the PhD in Data Science can be found in the NYU bulletin – Doctor of Philosophy in Data Science.
To be awarded the PhD in Data Science, students must, within 10 years of first enrolling:
- Complete 72 credit hours while maintaining a cumulative grade point average of 3.0 (out of 4.0) each semester.
- Complete the teaching requirement (for incoming students Fall 2020 and later).
- Pass a Comprehensive Exam.
- Pass the Depth Qualifying Exam (DQE) by May 15 of their fourth semester.
- Complete all the steps for approval of their PhD dissertation.
For more information on the PhD curriculum and requirements, please visit the PhD Student Handbook. Please note you will only be able to access the handbook through your NYU email address.
Required Course Information
Students must successfully complete the following courses by the end of their third semester unless otherwise stated or show evidence that they have taken equivalent coursework elsewhere. Recent course pages are linked below. Course descriptions can be found in NYU’s Albert Course Search.
- DS-GA 2003: Introduction to Data Science for PhD Students
- DS- GA 1002: Probability and Statistics for Data Science
- DS-GA 1003: Machine Learning
- DS-GA 1004: Big Data
- DS-GA 1005: Inference and Representation
- 18 credits of DS-GA 2001 – Research Rotation by end of 6th semester (for incoming students Fall 2020 and later).
- A research rotation is a semester-long guided research experience in which the student will have an opportunity to design and carry out original research in a collaborative setting. The idea is to help students identify research interests. PhD students take this course 6 times.
39 credit hours of elective courses (for incoming students starting Fall 2020 and later)
Students must successfully complete 39 credit hours of elective courses. Faculty at the Center for Data Science are experts in a broad range of data science topics, and the Center’s course offerings reflect that diversity. For example, students will be able to take courses in Deep Learning, Optimization, and Natural Language Processing.
Some of the electives offered at the Center for Data Science are below. Please see NYU’s Albert Course Search for course descriptions.
- Deep Learning (DS-GA 1008)
- Practical Training for Data Science (DS-GA 1009): Practical Training offers course credit for the academically relevant internship experience. This is an integral part of the PhD Program curriculum and facilitates students with academic and professional development. The course allows students to apply their academic and research knowledge to real-world problems.
- Independent Study (DS-GA 1010)
- Natural Language Processing with Representation Learning (DS-GA 1011)
- Natural Language Understanding and Computational Semantics (DS-GA 1012)
- Mathematical Tools for Data Science (DS-GA 1013)
- Optimization and Computational Linear Algebra (DS-GA 1014)
- Text as Data (DS-GA 1015)
- Computational Cognitive Modeling (DS-GA 1016)
- Responsible Data Science (DS-GA 1017)
- Probabilistic Time Series Analysis (DS-GA 1018)
- Communication Skills (DS-GA 2002)
Students can take electives outside of the Center of Data Science with permission from the Director of Graduate Studies (DGS).
Typical Schedule (Incoming Students Fall 2020 and later)
Typically, a student’s first 3 years will follow a schedule like the one outlined below. The student’s remaining years will consist of electives and work on his or her research and dissertation.
- First year, Fall: 2 required courses and 1 research rotation course
- DS-GA 2003 Introduction to Data Science for PhD Students
- DS-GA 1002 Probability and Statistics for Data Science
- DS-GA-2001 Research Rotation
- First-year, Spring: 2 required courses and 1 research rotation course
- DS-GA 1003 Machine Learning
- DS-GA 1004 Big Data
- DS-GA 2001 Research Rotation
- Second-year, Fall: 1 required course, and 1 research rotation course, and 1 elective; identify research advisor
- DS-GA 1005 Inference and Representation
- DS-GA 2001 Research Rotation
- Approved elective
- Second-year, Spring: Research rotation course, 2 electives; pass the Depth Qualifying Exam
- DS-GA 2001 Research Rotation
- Approved Elective
- Approved Elective
- Third-year, Fall: Research rotation course, 2 electives
- DS-GA 2001
- Approved Elective
- Approved Elective
- Third-year, Spring: Research rotation course, 2 electives
- DS-GA 2001 Research Rotation
- Approved Elective
- Approved Elective
Teaching Requirement (for incoming students starting Fall 2020 and later)
By the end of the fourth year of study, each student must have served as a section leader or instructor for at least two courses at the Center for Data Science (for students starting the program in Fall 2023 or later). For students who started the program between Fall 2020 – Fall 2022, the requirement is at least one course at the Center for Data Science.
Courses on related topics outside the Center may also be used to satisfy this requirement subject to approval by the DGS. The student must also participate in the Center’s teacher training session at or prior to the semester in which they teach. In certain circumstances, the DGS may allow the student to satisfy this requirement by serving as a course assistant or as a grader. These exceptions will be determined by the DGS based on the availability of suitable recitations.
Comprehensive Exam
The comprehensive exam is designed to determine whether the candidate displays the requisite data science knowledge to pursue their research.
For students starting the program in Fall 2024 and later: To fulfill this requirement, students will submit a 4-page report describing their work during their first year and a plan of their future research at the end of their second semester. The student will also give a 10-minute presentation in front of a pre-committee of three faculty (which will include their research advisors). The committee will determine whether the student is progressing adequately based on their academic performance (including grades and feedback from course instructors), the presentation, and the report.
For students who started the program prior to Fall 2024: The comprehensive exam consists of material from DS-GA 1003 Machine Learning and DS-GA 1004 Big Data. To fulfill this requirement, students must receive an A- or above as their final grade for each of the courses above (for students starting Fall 2020 – Fall 2023). Students are expected to complete this requirement by the end of their second semester.
Depth Qualifying Exam (DQE)
No later than the end of the third semester, each student must:
- Agree with a research advisor. The student is responsible for finding a research advisor, obtaining an agreement to advise the student, and informing the Director of Graduate Studies (DGS) of the agreement. Students must reach an agreement with the DGS and the Manager of Academic Affairs if they wish to change research advisors. If a research advisor determines that he or she no longer wishes to advise a student, the research advisor informs the DGS who will begin working with the student to find another research advisor.
- Agree with his or her research advisor on a research project, an exam topic, and a Depth Qualifying Exam (DQE) committee.
- Obtain the approval of the DGS on the research project, exam topic, and DQE committee, as well as the date of the DQE exam.
No later than the end of his fourth semester, the student must pass the depth qualifying exam (DQE). The exam may be taken no more than twice. The content of the exam is defined by the student’s DQE Committee, which must present a syllabus to the student at least 2 months before the date of the exam.
For incoming students Fall 2020 and later, the exam itself consists of a presentation by the student on original research carried out independently or in collaboration with faculty, research staff, or other students. This can include research done in the research rotations or other research conducted by the student in their area of interest. The goal of the DQE is to confirm the student’s knowledge of research in their area of interest.
PhD Dissertation
Dissertation Proposal Approval
CDS PhD students are encouraged to identify their dissertation proposal committee by the end of their second year. Students should consult with their advisor and/or the DGS. The student works with their research advisor to select a dissertation proposal approval committee, obtains approval of this committee from the DGS, submits a written dissertation proposal to the committee, and obtains the approval of the committee. The committee consists of at least three members, and may consist of individuals with similar standing outside of CDS. At least one member must be a CDS faculty member (CDS joint faculty member, member of the CDS PhD Advisory Group, or CDS affiliated (see the Areas & Faculty page). Students should have their dissertation proposal approved no later than the end of their third year. However, this is a guideline. Students are encouraged to identify timing of the dissertation proposal in consultation with their advisor and/or the DGS.
Dissertation Approval
A successful defense is required for award of the PhD.
The PhD defense committee must have at least 5 members, including the advisor(s), three of whom must be CDS faculty (CDS joint faculty member, member of the CDS PhD Advisory Group, or CDS affiliated (see Areas & Faculty page), and 1 external member (in related area from another NYU department or from an area institution, with approval from DGS). The membership of the defense committee is proposed by the student and approved by the DGS.
In addition, students must comply with all of the procedures of NYU’s Graduate School of Arts and Science related to the submission of their dissertation.